The rapid development of the Internet has brought people into an information society and network economy era, and has had a profound influence on both enterprise developments and personal lives. At the same time, however, an excessive amount of information makes it difficult for people to efficiently get what they need, lowering the efficiency of utilizing information.
E-commerce, for example, is a new business model in an open network environment, and offers browser/server based applications to allow online shopping, online transactions between merchants and online electronic payments. With the explosive development of the Internet, e-commerce is increasingly widespread. However, due to the development of supply chain and logistics, the types and quantities of goods on the Internet are so great that it greatly increases the time cost of consumer shopping, reduces the sales conversion rate of e-commerce platforms. Clearly, consumers do not want to spend too much time in searching the Internet for endless merchandises. Furthermore, online shopping does not allow shoppers to check the quality of the goods, as in real life. Shoppers desire an automatic recommendation system which can make suggestions according to their own interests and satisfaction. Targeting personal recommendations according to the different user profiles or by clustering users into different user groups, is one of the current trending applications.
In the prior art, user clustering is based on a sequence of webpage hits from user access paths or user search keywords. Because user access is generally repeated and interrupted, each visitor's click path cannot be exactly the same every time. As a result, the existing technology is unable to balance the differences caused by multiple user visits, resulting in poor user clustering effect and inefficient service.
Therefore, there is currently a pressing need to solve the following technical problem: providing personalized service recommendations, which can accurately measure the correlation between users to form an effective and accurate user group, and provide targeted services with improved efficiency.